Systematic literature review (SLR): Comparative study of the performance of skin disease detection algorithms on visible light and infrared images

Some algorithms tend to be more effective on visible light images, while others can excel at extracting features from infrared images. This research aims to understand the Performance Comparison of Skin Disease Detection Algorithms, identify the Advantages of Infrared Images and Visible Light Images, and measure the Effectiveness and Reliability of Skin Disease Detection Algorithms on Infrared Images and Visible Light Images. The research results show that the skin disease detection algorithm in infrared images and visible light images needs to be evaluated comprehensively to understand its performance. Performance metrics such as accuracy, precision, recall, and F1-score can be used to compare detection results between the two types of images. Visible light images provide information about skin color and surface structure with high resolution, while infrared images provide information on skin temperature. Both can provide different perspectives depending on the type of skin disease. The detection algorithm must have sufficient sensitivity to detect changes in both types of images. Reliability algorithms on infrared images may require special adaptations because their characteristics are different from visible light images. Performance evaluation includes measuring accuracy, precision, recall, and F1-score on both types of images.